There have also been so many methods to multi-agent reinforcement learning [1]}, [2]}, [3]}, in which, there are a number of agents operating within a single shared environment, each of which aims to optimize its long-term return by interacting with the environment and other agents. However, in this article, we review the papers seeking to solve a single-agent problem by exploiting parallel computation and decentralized data generation. Some works apply several distributed frameworks (like MapReduce) in large matrix multiplications [4]}. Nevertheless, they are not applicable to non-linear representations, so we do not review them.
